This grant proposal addresses the following important problems in computational proteomics: 1. Development of new filters for MS/MS database searches that will dramatically reduce the running time for protein identification and post-translationally modified proteins in particular. 2. Design of new algorithms for matching MS/MS spectra against the alternative splicing databases. 3. Development of algorithms for shotgun protein sequencing by clustering and assembly of overlapping spectra. Application of clustering and assembly of MS/MS spectra to analysis of post - translational modifications. Improving the state of the art in de novo sequencing through analysis of paired MS/MS spectra and generation of reliable sequence tags derived from paired spectra. 4. Development of computational tools for analyzing relative abundance of peptides in protein samples. [unreadable] Relevance: Mass spectromtery is a key technology for proteomics, and is increasingly used for research that directly impacts human health. Examples include, but are not limited to, discovery of protein bio-markers that can be used as diagnostics, and small peptides that can be used directly as therapeutics. However, computational analysis of mass spectromtery data remains a significant bottleneck. This proposed research addresses computational challenges in the analysis of mass spectromtery data. [unreadable] [unreadable] [unreadable] [unreadable]